A Novel Robust Image Forensics Algorithm Based on L1-Norm Estimation

To improve the robustness of the typical image forensics with the noise variance, we propose a novel image forensics approach that based on L1-norm estimation. First, we estimate the kurtosis and the noise variance of the high-pass image. Then, we build a minimum error objective function based on L1-norm estimation to compute the kurtosis and the noise variance of overlapping blocks of the image by an iterative solution. Finally, the spliced regions are exposed through K-means cluster analysis. Since the noise variance of adjacent blocks are similar, our approach can accelerate the iterative process by setting the noise variance of the previous block as the initial value of the current block. According to analytics and experiments, our approach can effectively solve the inaccurate locating problem caused by outliers. It also performs better than reference algorithm in locating spliced regions, especially for those with realistic appearances, and improves the robustness effectively.